REMARKS ON EVOLUTION AND TIME-SCALES, Graham Cairns-Smith; HODGSON'S BLACK BOX, Thomas Clark; DO HODGSON'S PROPOSITIONS UNIQUELY CHARACTERIZE FREE WILL?, Ravi Gomatam; WHAT SHOULD WE RETAIN FROM A PLAIN PERSON'S CONCEPT OF FREE WILL?, Gilberto Gomes; ISOLATING DISPARATE CHALLENGES TO HODGSON'S ACCOUNT OF FREE WILL, Liberty Jaswal; FREE AGENCY AND LAWS OF NATURE, Robert Kane; SCIENCE VERSUS REALIZATION OF VALUE, NOT DETERMINISM VERSUS CHOICE, Nicholas Maxwell; COMMENTS ON HODGSON, J.J.C. Smart; THE VIEW FROM WITHIN, Sean Spence; COMMENTARY ON HODGSON, Henry (...) Stapp. (shrink)

The paper discusses Dr. Floris Tomasini's paper “What Is Bioethics: Notes toward a New Approach?”. Based on Tomasini's account of methodological and ethical pluralism, the paper explores the demarcation problem of bioethics and suggests a full methodological laissez-faire.

© 2017. The American Astronomical Society. All rights reserved.We present new observations of the low-mass companion to HD 984 taken with the Gemini Planet Imager as a part of the GPI Exoplanet Survey campaign. Images of HD 984 B were obtained in the J and H bands. Combined with archival epochs from 2012 and 2014, we fit the first orbit to the companion to find an 18 au orbit with a 68% confidence interval between 14 and 28 au, an eccentricity (...) of 0.18 with a 68% confidence interval between 0.05 and 0.47, and an inclination of 119°with a 68% confidence interval between 114°and 125°. To address the considerable spectral covariance in both spectra, we present a method of splitting the spectra into low and high frequencies to analyze the spectral structure at different spatial frequencies with the proper spectral noise correlation. Using the split spectra, we compare them to known spectral types using field brown dwarf and low-mass star spectra and find a best-fit match of a field gravity M6.5 ±1.5 spectral type with a corresponding temperature of K. Photometry of the companion yields a luminosity of log=2.88 ± 0.07 dex with DUSTY models. Mass estimates, again from DUSTY models, find an age-dependent mass of 34 ±1 to 95 ±4 M Jup. These results are consistent with previous measurements of the object. (shrink)

Part I: The Life of Cognitive Science:. William Bechtel, Adele Abrahamsen, and George Graham. Part II: Areas of Study in Cognitive Science:. 1. Analogy: Dedre Gentner. 2. Animal Cognition: Herbert L. Roitblat. 3. Attention: A.H.C. Van Der Heijden. 4. Brain Mapping: Jennifer Mundale. 5. Cognitive Anthropology: Charles W. Nuckolls. 6. Cognitive and Linguistic Development: Adele Abrahamsen. 7. Conceptual Change: Nancy J. Nersessian. 8. Conceptual Organization: Douglas Medin and Sandra R. Waxman. 9. Consciousness: Owen Flanagan. 10. Decision Making: J. Frank (...) Yates and Paul A. Estin. 11. Emotions: Paul E. Griffiths. 12. Imagery and Spatial Representation: Rita E. Anderson. 13. Language Evolution and Neuromechanisms: Terrence W. Deacon. 14. Language Processing: Kathryn Bock and Susan M. Garnsey. 15. Linguistics Theory: D. Terence Langendoen. 16. Machine Learning: Paul Thagard. 17. Memory: Henry L. Roediger III and Lyn M. Goff. 18. Perception: Cees Van Leeuwen. 19. Perception: Color: Austen Clark. 20. Problem Solving: Kevin Dunbar. 21. Reasoning: Lance J. Rips. 22. Social Cognition: Alan J. Lambert and Alison L. Chasteen. 23. Unconscious Intelligence: Rhianon Allen and Arthur S. Reber. 24. Understanding Texts: Art Graesser and Pam Tipping. 25. Word Meaning: Barbara C. Malt. Part III: Methodologies of Cognitive Science:. 26. Artificial Intelligence: Ron Sun. 27. Behavioral Experimentation: Alexander Pollatsek and Keith Rayner. 28. Cognitive Ethology: Marc Bekoff. 29. Deficits and Pathologies: Christopher D. Frith. 30. Ethnomethodology: Barry Saferstein. 31. Functional Analysis: Brian Macwhinney. 32. Neuroimaging: Randy L. Buckner and Steven E. Petersen. 33. Protocal Analysis: K. Anders Ericsson. 34. Single Neuron Electrophysiology: B. E. Stein, M.T. Wallace, and T.R. Stanford. 35. Structural Analysis: Robert Frank. Part IV: Stances in Cognitive Science:. 36. Case-based Reasoning: David B. Leake. 37. Cognitive Linguistics: Michael Tomasello. 38. Connectionism, Artificial Life, and Dynamical Systems: Jeffrey L. Elman. 39. Embodied, Situated, and Distributed Cognition: Andy Clark. 40. Mediated Action: James V. Wertsch. 41. Neurobiological Modeling: P. Read Montague and Peter Dayan. 42. Production Systems: Christian D. Schunn and David Klahr. Part V: Controversies in Cognitive Science:. 43. The Binding Problem: Valerie Gray Hardcastle. 44. Heuristics and Satisficing: Robert C. Richardson. 45. Innate Knowledge: Barbara Landau. 46. Innateness and Emergentism: Elizabeth Bates, Jeffrey L. Elman, Mark H. Johnson, Annette Karmiloff-Smith, Domenico Parisi, and Kim Plunkett. 47. Intentionality: Gilbert Harman. 48. Levels of Explanation and Cognition Architectures: Robert N. McCauley. 49. Modularity: Irene Appelbaum. 50. Representation and Computation: Robert S. Stufflebeam. 51. Representations: Dorrit Billman. 52. Rules: Terence Horgan and John Tienson. 53. Stage Theories Refuted: Donald G. Mackay. Part VI: Cognitive Science in the Real World:. 54. Education: John T. Bruer. 55. Ethics: Mark L. Johnson. 56. Everyday Life Environments: Alex Kirlik. 57. Institutions and Economics: Douglass C. North. 58. Legal Reasoning: Edwina L. Rissland. 59. Mental Retardation: Norman W. Bray, Kevin D. Reilly, Lisa F. Huffman, Lisa A. Grupe, Mark F. Villa, Kathryn L. Fletcher, and Vivek Anumolu. 60. Science: William F. Brewer and Punyashloke Mishra. Selective Biographies of Major Contributors to Cognitive Science: William Bechtel and Tadeusz Zawidzki. (shrink)

The neural correlates of rapid eye movements (REMs) in sleep are extraordinarily robust; including REM-locked activation in the retrosplenial cortex, the supplementary eye field and areas overlapping cholinergic basal nucleus. The phenomenology of REMs speaks to the notion that perceptual experience in both sleep and wakefulness is a constructive process – in which we generate predictions of sensory inputs and then test those predictions through actively sampling the sensorium with eye movements. On this view, REMs during sleep may index an (...) internalized active sampling or ‘scanning’ of self-generated visual constructs that are not constrained by visual input. If this view is correct, it renders REMs an ideal probe to study consciousness as “an exclusively internal affair” (Metzinger 2009). In other words, REMs offer a probe of active inference – in the sense of predictive coding – when the brain is isolated from the sensorium in virtue of the natural blockade of sensory input during REM sleep. REMs are temporally precise events that enable powerful inferences based on timeseries analyses. As a natural, task-free probe, [REMs] could be used in non-compliant subjects, including infants and animals. In short, REM constitutes a promising probe to study the ontogenetic and phylogenetic development of consciousness and perhaps its abnormal development in schizophrenia and autism, which have been considered in terms of aberrant predictive coding. (shrink)

It is traditionally believed that cerebral activation (the presence of low voltage fast electrical activity in the neocortex and rhythmical slow activity in the hippocampus) is correlated with arousal, while deactivation (the presence of large amplitude irregular slow waves or spindles in both the neocortex and the hippocampus) is correlated with sleep or coma. However, since there are many exceptions, these generalizations have only limited validity. Activated patterns occur in normal sleep (active or paradoxical sleep) and during states of anesthesia (...) and coma. Deactivated patterns occur, at times, during normal waking, or during behavior in awake animals treated with atropinic drugs. Also, the fact that patterns characteristic of sleep, arousal, and waking behavior continue in decorticate animals indicates that reticulo-cortical mechanisms are not essential for these aspects of behavior. (shrink)

In orthodox quantum mechanics, it has virtually become the custom to identify properties of a physical system with operationally testable propositions about the system. The causes and consequences of this practice are explored mathematically in this paper. Among other things, it is found that such an identification imposes severe constraints on the admissible states of the physical system.